Project Summary Automated insulin delivery (AID) systems offer substantial opportunities for helping people with type 1 diabetes (T1D) to improve glucose control and lower HbA1c. However, the AID has only shown a benefit during the nighttime when meals, exercise, and stress do not significantly challenge the AID. Furthermore, hypoglycemia (<70 mg/dL) remains a common occurrence in people with type 1 diabetes and continues to occur even in the setting of AID, particularly with exercise. Integrating context awareness into an AID has the potential to improve glycemic time in range (70-180 mg/dL) during the daytime and reduce and possibly eliminate hypoglycemia. Contextual information can include inferred food intake, insulin dosing, inferred exercise type and duration, as well as movement patterns. An AID can be designed to recognize contextual patterns that relate to poor glycemic responses to meals and hypoglycemia and then adjust insulin dosing in response to these patterns in advance and help mitigate these problems. In this grant, we will explore how contextual information may be used within an AID to help (1) avoid hypoglycemia and (2) reduce postprandial dysglycemia. We will first conduct a data gathering study whereby we will collect a rich data set from people with T1D who will use sensor augmented pump therapy to manage their glucose. Data will be collected from these 30 patients over 28 days; data will include multivariable contextual information including continuous glucose monitoring (CGM) data, insulin data, food data, physical activity data (heart rate and accelerometry), as well as indoor/outdoor contextual movement patterns gathered using a novel beacon-based context-aware sensing system called MotioWear developed by our group in collaboration with our industry partner MotioSens. Next, we will utilize this contextual data set to construct a Bayesian glucose prediction algorithm. This will include a clustering algorithm that will group contextual sequences that are similar with each other and which lead to similar glycemic outcomes. This context-aware glucose prediction algorithm will be integrated into an adaptive, personalized, smartwatch-based context-aware AID (CA-AID) system. Contextual patterns that have a high likelihood of leading to hypoglycemia or postprandial dysglycemia will inform an insulin dosing aggressiveness factor to be adjusted for similar contextual sequences observed in the future (i.e. the CA-AID will reduce insulin for contextual sequences with high likelihood of hypoglycemia such as aerobic exercise). We expect that integrating context awareness into an AID will lead to significant improvements in time in target range during the day and will help reduce time in hypoglycemia. The CA-AID will be evaluated for safety in a small pilot study. We will then evaluate the CA-AID within a 6 week clinical study in 40 adults with type 1 diabetes on insulin pump therapy. Twenty will receive the CA-AID while the other 20 will receive a standard (non-context-aware) AID. The primary outcome measures of this study is the percent time in range. We hypothesize that the CA-AID will increase time in range by 10% as compared with a non-context aware AID.